Data Scientists – Who Are They And What Do They Do?

In a startup, you may find a data scientists donning several roles as a data engineer or an architect, researcher, statistician, modeler, data miner, executive, developer, etc. So what exactly do data scientists do? Are not they coders who know their R, SQL, Pythons, statistics, and Hadoop well? Yes, and a lot more. Data scientists are coders, but they have expertise in various fields ranging from bioinformatics, simulations and quality control, computational finance, industrial engineering, information technology, epidemiology to number theory.

I have dedicated the last 10 years of my life to developing automated systems for processing large data sets and transactions, for example, generate content automatically. The system is a combination of AI (Artificial intelligence), IoT (internet of things) and data science which is called deep data science, which is almost math-free, code-free except (API's) but at the same time data intensive. I have also worked on credit card fraud detection and image remote sensing technology, and I was required to identify patterns in the vast number of satellite images and do image segmentation. My work fell under computational statistics whereas the same work done by my peers at my home university was called artificial intelligence. Today, it can be called both but subdomains being computer vision, IoT and signal processing. 

The Types of Data Scientists you are Likely to Encounter

Ajit Jaokar has very neatly categorized and explained different types of data scientists. There are two types, A and B. The type A data scientists are the not expert coders, as you may have assumed, but are rather have a strong statistical basis and are experts in statistical inference, modeling, experimental design, and forecasting. This type of data scientist at Google can be a statistician, decisions support engineering analyst, quantitative analyst or data scientist. Type B data scientists are the ones who are expert coders but know their basic statistics. They are mostly builders who create or develop programs for user interaction like proving recommendations. 

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The Difference Between Data Science and Machine Learning

There is not much of a difference between the two, considering machine learning is actually a part of data science. Data science has a broader spectrum than machine learning since it covers not only statistical or algorithm aspects of data, but it also includes the following-
  • Distributed architecture
  • Data visualization
  • Automated machine learning
  • Dashboards and BI
  • Data engineering
  • Data integration
  • Deployment in production mode
  • Automated, data-driven decisions. 
Although, data scientists can choose to work only on one part of the entire process.

The Difference between Machine Learning and Deep Learning

Machine learning is an algorithm which is dependent on a trained data either to take actions or to make some predictions. It uses several techniques for that which includes, regressive, supervised clustering, unsupervised clustering as well as semi-supervised clustering and sometimes a combination of many such methods. When these algorithms are made to be automatic they are called AI (artificial intelligence) or deep learning.. it becomes deep learning when the data is transmitted over the internet.

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